TOON is an innovative data serialization format designed specifically to optimize calls to large language models. Unlike JSON, which requires verbose syntax with repetitive quotes and braces, TOON drastically reduces token consumption—a directly billable resource in LLM interactions. The project demonstrates reductions of 30 to 60% in tokens compared to JSON, with some scenarios reaching up to 65% savings depending on the structured data.

The format's architecture merges concepts from YAML (indentation for hierarchy) with CSV (tabular format for uniform data). This hybrid approach proves particularly effective for repetitive structures—collections of identical objects where each record shares the same fields. The minimal syntax eliminates redundant delimiters, using only spaces to indicate nesting and commas for internal separations.

Technically, TOON encodes critical metadata in array headers: key[N]{field1,field2}: indicates N elements with specified fields. This explicit approach improves validation by LLMs and facilitates parsing of complex structures. Benchmarks reveal a data retrieval accuracy of 86.6% versus 83.2% for JSON, demonstrating that compactness does not sacrifice model comprehension.

Use cases range from analytical data exports to GitHub repository lists, including nested e-commerce orders. For organizations managing massive volumes of LLM requests, this optimization generates substantial savings on API costs, while developers benefit from syntax that is more readable than conventional binary compression.

The project, created on October 22, 2025 by Johann Schopplich, represents a pragmatic response to a concrete economic problem: every token consumed in an LLM interaction has a direct financial cost. By halving JSON's verbosity while maintaining—or even improving—model comprehension, TOON offers an immediate competitive advantage for AI-intensive applications.

The innovation lies in the balance between technical optimization and human readability. Unlike binary compression formats that become opaque, TOON remains interpretable, facilitating debugging and maintenance. This characteristic proves crucial in production environments where the transparency of data exchanged with LLMs becomes a governance and compliance concern.